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        <title>Dual Supervised Learning for Natural Language Understanding and Generation</title>
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    <name type="personal">
        <namePart type="given">Shang-Yu</namePart>
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        <namePart type="given">Chao-Wei</namePart>
        <namePart type="family">Huang</namePart>
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    <name type="personal">
        <namePart type="given">Yun-Nung</namePart>
        <namePart type="family">Chen</namePart>
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            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</title>
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    <abstract>Natural language understanding (NLU) and natural language generation (NLG) are both critical research topics in the NLP and dialogue fields. Natural language understanding is to extract the core semantic meaning from the given utterances, while natural language generation is opposite, of which the goal is to construct corresponding sentences based on the given semantics. However, such dual relationship has not been investigated in literature. This paper proposes a novel learning framework for natural language understanding and generation on top of dual supervised learning, providing a way to exploit the duality. The preliminary experiments show that the proposed approach boosts the performance for both tasks, demonstrating the effectiveness of the dual relationship.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1545</identifier>
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        <date>2019-jul</date>
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%0 Conference Proceedings
%T Dual Supervised Learning for Natural Language Understanding and Generation
%A Su, Shang-Yu
%A Huang, Chao-Wei
%A Chen, Yun-Nung
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 jul
%I Association for Computational Linguistics
%C Florence, Italy
%F su-etal-2019-dual
%X Natural language understanding (NLU) and natural language generation (NLG) are both critical research topics in the NLP and dialogue fields. Natural language understanding is to extract the core semantic meaning from the given utterances, while natural language generation is opposite, of which the goal is to construct corresponding sentences based on the given semantics. However, such dual relationship has not been investigated in literature. This paper proposes a novel learning framework for natural language understanding and generation on top of dual supervised learning, providing a way to exploit the duality. The preliminary experiments show that the proposed approach boosts the performance for both tasks, demonstrating the effectiveness of the dual relationship.
%R 10.18653/v1/P19-1545
%U https://aclanthology.org/P19-1545
%U https://doi.org/10.18653/v1/P19-1545
%P 5472-5477

Markdown (Informal)

[Dual Supervised Learning for Natural Language Understanding and Generation](https://aclanthology.org/P19-1545) (Su et al., ACL 2019)

• Dual Supervised Learning for Natural Language Understanding and Generation (Su et al., ACL 2019)

ACL

• Shang-Yu Su, Chao-Wei Huang, and Yun-Nung Chen. 2019. Dual Supervised Learning for Natural Language Understanding and Generation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5472–5477, Florence, Italy. Association for Computational Linguistics.